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    Adaptive models and heavy tails with an application to inflation forecasting

    Delle Monache, D. and Petrella, Ivan (2016) Adaptive models and heavy tails with an application to inflation forecasting. Working Paper. Birkbeck, University of London, London, UK.

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    This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coefficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of in ation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of fit and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI in ation rate and are confirmed by other in ation indicators, as well as for CPI in ation of the other G7 countries.


    Item Type: Monograph (Working Paper)
    Additional Information: BCAM 1603; ISSN 1745-8587
    Keyword(s) / Subject(s): adaptive algorithms, in ation, score-driven models, student-t, timevarying parameters
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Research Centres and Institutes: Applied Macroeconomics, Birkbeck Centre for
    Depositing User: Administrator
    Date Deposited: 21 Mar 2019 16:20
    Last Modified: 02 Aug 2023 17:49


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